Code Interpreter

What it demonstrates

A “hosted” tool runs inside the provider, not in your process: the model writes Python, the provider executes it in a sandbox, and the result flows back into the conversation. It’s perfect for exact arithmetic, data crunching, and file processing — things a language model guesses badly but a Python interpreter nails.

The key difference from a function tool: you don’t register a Python function. You just hand the agent the hosted tool and the model decides when to reach for it.

The code

return Agent(
    client=client,
    name="coder",
    instructions=(
        "You are a helpful assistant that writes and executes Python code "
        "to solve problems. Prefer running code over estimating answers."
    ),
    tools=[FoundryChatClient.get_code_interpreter_tool()],
)

The generated code and its output arrive as separate content parts on the result messages — type code_interpreter_tool_call (the code) and code_interpreter_tool_result (the execution output) — while result.text holds the final answer.

The gotcha

Foundry exposes the tool through the classmethod FoundryChatClient.get_code_interpreter_tool() passed in tools=[...] — you do not write your own @tool function for this. Availability is provider-dependent (Azure AI Foundry supports it). And if you want to see what the sandbox actually did, iterate result.messages and inspect the structured content parts; the raw code and its stdout are not folded into result.text, they ride along as separate parts.

Azure / MAF mapping

This is a hosted capability of Azure AI Foundry, not a local callable — the client is a FoundryChatClient with AzureCliCredential(), and the code runs server-side in Foundry’s sandbox. Contrast with the function-tools lesson, where your Python ran in-process; here the model’s Python runs in the provider.

Run it

uv run tutorial/02-agents/tools/04_code_interpreter.py — needs Foundry creds. It prints the generated code, its execution output, then the answer (e.g. factorial of 100 has 158 digits).

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